This study focuses on the various policies that the US Federal government can enact to increase COVID-19 vaccination rates and stabilize the spread of COVID-19 with herd immunity. It compares a vaccine mandate enforced with a fine, the policy in question, to various incentives such as tax rebates and sporting tickets. It also takes into account the accessibility of vaccination centers, measured by the proximity of vaccination centers from residencies, as well as combinations of these different policy attributes. The target population for this study are all Americans 18 years and older who have not received a dose of a COVID-19 vaccine. Below are lists of all possible values of each attribute included in the choice questions:
The results for our study show a high confidence interval for all attributes, plus a positive coefficient for distance to a vaccination center - our respondents had slightly higher preference towards further vaccination centers, an unlikely and unexpected result. The variance and accuracy of our coefficients reemphasizes that we need a larger and more varied sample to get a more accurate and definite estimate. The positive value for distance to a vaccination center also shows a possible interaction effect with another attribute. This informs us that to get accurate readings, we will need to study both interaction and main effects of attributes. To do so, a full factorial design with every possible choice attribute combination will be the most optimal, and we aim to get a sample size as large as possible by reaching out to others in the GW community and across the US.
Despite the rollout of COVID-19 vaccines, vaccine hesitancy prevented the United States from achieving herd immunity, the level of immunity that a population has to control the spread of a virus. Below is a chart depicting the number of daily vaccinations over time as a percentage of the population, followed by a chart depicting the percentage of the American population that is vaccinated:
In turn, the Delta variant, a highly-contagious strain of SARS-COV-2, the virus that causes COVID-19, ravaged through unvaccinated populations and caused a surge in cases.
On September 9th, 2021, U.S. President Joe Biden announced vaccine mandates for 100 million Americans, including federal employees and private-sector employees part of firms with over 100 people. This has been met with praise from some experts who claim that forcing Americans to get vaccinated is crucial, criticism from other experts who claim that resistance to vaccine mandates will cause labor shortages and is not effective enough to vaccinate enough of the population [for herd immunity], and hostile resistance, particularly from conservatives, who attack the Constitutional provision of the mandates. View this article to read some of the reactions to Biden’s order.
Vaccine mandates have been touted for months, since the COVID-19 vaccine has become available. But before they were enacted many state and local governments started giving out positive incentives such as money or commodities. All levels of government, from the federal government to local, enacted initiatives to increase access to the vaccine by fighting misinformation and setting up mobile vaccination sites.
Our study analyzes the population and the effectiveness of various policies to increase vaccination rates. Each policy in our study consists of different attributes, which vary across each policy. The goal is to optimize the attributes to get as many people vaccinated as possible. The attributes to be considered are:
Accessibility refers to the distance which a vaccination clinic would be placed from your home. It varies with 0 (door-door vaccinations) - 10 miles from your residence.
Incentives refers to the type of commodity that would be given out at tax season for being fully vaccinated against COVID-19 by the tax return deadline. It varies by cash tax rebates and cash equivalents (gift cards) redeemable at:
Incentive value refers to the value of commodities that would be given out at tax season for being fully vaccinated against COVID-19 by the tax return deadline. It varies by increasing increments from $0 to $1000.
Incentive value refers to the penalty due at tax season for failing to get a being fully vaccinated against COVID-19 by the tax return deadline under a vaccine mandate. It varies by increasing increments from $0 to $1000.
In order to see what sort of policy would be most appealing to those who are currently unvaccinated, the survey is designed to only collect responses from those who would be able to choose to take the vaccine after seeing the policy options. This consists of adults 18 years or older who have not received any doses of a COVID-19 vaccine. Those under the age of 18 and who have already received a vaccine dose were excluded because they are unable to choose to take the vaccine. This survey is also restricted to United States residents only, as this policy would only be directed towards United States residents.
The survey also collects demographic information relating to age, gender identity, race, income, political affiliation, and geographic location to better inform which policies are most effective for which demographics. Educational material presented to the respondents include information on accessibility, incentives, incentive value, and mandate value, which make up the survey’s attributes.
Each respondent receives 10 choice questions that have two options “I will take the vaccine under these conditions” (where the conditions are listed in the option) and “I will not take the vaccine under these conditions”. There is one alternative per choice question (the vaccine conditions) and a no-choice option, where respondents are informed that it means they would not take the vaccine under the conditions listed in the first alternative. An example figure of a random conjoint question can be seen below.
The final pilot survey sample after cleaning consists of 107 respondents. The choice data sheet contains of 20 rows per respondent, denoting 2 alternatives per question (will you take the vaccine under this condition or not?) and 10 questions each.
When we first opened the responses from the three separate components, we noticed a lot of null values to numerous attributes, including session ids that seemed to be from unsuccessful attempts to view our survey, or from bots on the internet. To remove these values gracefully, we had to join these three components together by the session id, a unique identifier for each participant associated with the survey. To do so, we took out duplicate columns and removed null values for session. We also computed the time took to complete each survey component in unique columns. Then, we filtered out respondents that did not answer the choice questions and did not meet eligibility criteria. We only filtered out respondents based on if they didn’t answer any choice questions because no one only partially answered th choice questions, so we could filter out only those who didn’t answer any. There were 697 total respondents, but we trimmed it down to 107 because many did not meet the eligibility criteria and were not able to get to the choice questions. We also filtered out respondents who didn’t pass the attention check, but no one was filtered out in this process as every respondent passed.
Our utility model for each policy attribute is as follows:
$ Uj = {value}x^{value} + {penalty}x^{penalty} +{accessibility_0}^{accessibility_0} + {accessibility_1}^{accessibility_1} +{accessibility_3}^{accessibility_3} + {accessibility_10}^{accessibility_1_0} + {cash}^{cash} + {groceries}^{groceries} + {internet}^{internet} + {sports}^{sports} $
The first two beta values represent continuous attributes, and for every change in value and penalty, the utility for policy j will increase by the beta value for that attribute * the level of the attribute. The last 8 beta values are for categorical attributes that can either be 0 or 1, with 0 denoting the attribute is not present and 1 denoting the presence of an attribute. If present, the utility for policy j will increase by the beta value for that attribute. The first four of those 8 beta values represent categorical variables for distance to a vaccination center, in miles from respondents’ residences. The first accessibility coefficient represents door-door vaccination campaigns. The last four terms represents the type of incentive given out - cash, grocery store gift cards, internet rebates, or sporting event tickets.
Using the model and the choice data, we then ran the logitr model using preference space to estimate the beta coefficients. Running a summary of the results returned to us the estimated coefficients and the standard error.
| coefficients | st_errors | |
|---|---|---|
| value | 0.0005111 | 0.0001911 |
| penalty | -0.0004686 | 0.0002416 |
| accessibility_1 | 0.0804890 | 0.1857847 |
| accessibility_3 | 0.0735859 | 0.1837196 |
| accessibility_10 | 0.0285740 | 0.1868493 |
| incentive_grocery_store | 0.5278555 | 0.1803210 |
| incentive_internet | 0.1210913 | 0.1912552 |
| incentive_sport_tickets | 0.0098003 | 0.1928311 |
| outsideGood | 0.9830817 | 0.1963739 |
We ran the mixed logit model on the data. We tried multiple combinations of attributes to vary with a normal distribution, and found that the optimal parameters were value, penalty, and every incentive type varying across a normal distribution. We also found that since the mixed logit model is not convex, using 50 multistarts optimizes the model. We tried multistarts ranging from 2 to 100, and found that the optimal model is found after 50. However, we found that even the optimal mixed logit model is not a good fit, as the expected value for each of the coefficients does not mirror the expected values of the coefficients when using a multinomial logit model.
| coefficients | st_errors | |
|---|---|---|
| value_mu | 0.0022262 | 0.0007514 |
| penalty_mu | -0.0008791 | 0.0021181 |
| accessibility_1 | 0.5889784 | 0.6222866 |
| accessibility_3 | -0.5088493 | 0.7641872 |
| accessibility_10 | 0.2904307 | 0.6313879 |
| incentive_grocery_store_mu | 15.3158601 | 13.0733657 |
| incentive_internet_mu | 0.0407058 | 0.5707278 |
| incentive_sport_tickets_mu | 3.2118784 | 3.2823391 |
| outsideGood | 1.1480483 | 0.5721541 |
| value_sigma | 0.0000108 | 0.0014957 |
| penalty_sigma | 0.0136262 | 0.0118530 |
| incentive_grocery_store_sigma | 43.4853586 | 36.9758657 |
| incentive_internet_sigma | 3.3756724 | 2.8526296 |
| incentive_sport_tickets_sigma | 12.8052311 | 12.9609789 |
We ran the logitr model with resistant respondents, those who chose not to take the vaccine under all conditions (denoted by outsideGood). We called this subgroup the “resistant” group. We obtained this group by marking each respondent with a 0 or 1 denoting if they answered “no” to all choice questions, and running the logitr model with this group using separate attributes.
| coefficients | st_errors | |
|---|---|---|
| value | 0.0007100 | 0.0002828 |
| penalty | -0.0004837 | 0.0003302 |
| accessibility_1 | 0.1958957 | 0.2670157 |
| accessibility_3 | 0.0587616 | 0.2653845 |
| accessibility_10 | 0.1885783 | 0.2714119 |
| incentive_grocery_store | 0.5945227 | 0.2620557 |
| incentive_internet | 0.1087567 | 0.2655115 |
| incentive_sport_tickets | 0.2330551 | 0.2847370 |
| outsideGood | 0.4305177 | 0.2766491 |
| outsideGood_resistant | 0.9852725 | 0.4126940 |
| value_resistant | -0.0003627 | 0.0004121 |
| penalty_resistant | -0.0002322 | 0.0005347 |
| accessibility_1_resistant | -0.3975556 | 0.4015675 |
| accessibility_3_resistant | -0.0384331 | 0.3870407 |
| accessibility_10_resistant | -0.3220526 | 0.3963647 |
| incentive_grocery_store_resistant | -0.1738697 | 0.3832617 |
| incentive_internet_resistant | -0.1735595 | 0.4140298 |
| incentive_sport_tickets_resistant | -0.2535831 | 0.4106109 |
We ran the logitr model with conservative, liberal, and independent respondents, who self-identified with each respective political party. We obtained these groups by making dummmy columns for each unique political orientation marking each respondent with a 0 or 1 denoting if they have a specific political orientation. We decided to narrow down the number of political groups to reflect our sample size by merging “strongly liberal” and “leaning liberal” into “liberal” and merging “strongly conservative” and “leaning conservative” into “conservative”. We then ran the logitr model on these groups and independents using unique attributes.
| coefficients | st_errors | |
|---|---|---|
| value | 0.0007494 | 0.0004551 |
| penalty | -0.0007930 | 0.0005237 |
| accessibility_1 | -0.3628354 | 0.3043856 |
| accessibility_3 | -0.5992260 | 0.3218997 |
| accessibility_10 | -0.3049389 | 0.3573930 |
| incentive_grocery_store | 0.8077133 | 0.3839628 |
| incentive_internet | -0.2547931 | 0.4246870 |
| incentive_sport_tickets | 0.3149509 | 0.4401965 |
| outsideGood | 0.4124942 | 0.3951194 |
| value_liberal | 0.0000072 | 0.0005852 |
| penalty_liberal | 0.0005274 | 0.0006997 |
| accessibility_1_liberal | 0.5646333 | 0.2949376 |
| accessibility_3_liberal | 0.8355463 | 0.2894474 |
| accessibility_10_liberal | 0.4735110 | 0.4454765 |
| incentive_grocery_store_liberal | -0.3286184 | 0.5213877 |
| incentive_internet_liberal | -0.0561608 | 0.5525032 |
| incentive_sport_tickets_liberal | -0.3111666 | 0.5638608 |
| outsideGood_liberal | -0.2461253 | 0.4944326 |
| value_conservative | 0.0002351 | 0.0006199 |
| penalty_conservative | 0.0004864 | 0.0007465 |
| accessibility_1_conservative | 0.9932580 | 0.5398139 |
| accessibility_3_conservative | 1.1545165 | 0.5475154 |
| accessibility_10_conservative | 0.5914997 | 0.6041454 |
| incentive_grocery_store_conservative | -1.1227352 | 0.5906551 |
| incentive_internet_conservative | 0.6716591 | 0.5821913 |
| incentive_sport_tickets_conservative | -0.8618877 | 0.6382369 |
| outsideGood_conservative | 2.0021718 | 0.6260375 |
| value_independent | -0.0006199 | 0.0006589 |
| penalty_independent | 0.0006692 | 0.0007991 |
| accessibility_1_independent | 0.3203842 | 0.5878214 |
| accessibility_3_independent | 0.9647243 | 0.5580106 |
| accessibility_10_independent | -0.1226066 | 0.6310279 |
| incentive_grocery_store_independent | -0.2292768 | 0.6019469 |
| incentive_internet_independent | 0.8813157 | 0.6527790 |
| incentive_sport_tickets_independent | 0.0599380 | 0.6483903 |
| outsideGood_independent | 1.0120944 | 0.6364934 |
We ran the logitr model with low-income and high-income respondents, who self-identified with each respective income group. We obtained these groups by making dummmy columns for each income bracket and marking each respondent with a 0 or 1 denoting if they fall in a particular income group. We decided to narrow down the number of political groups to reflect our sample size by merging respondents who earned less than $50,000 into the low-income group and merging respondents who earned $100,000 oe more into the high-income group. We then ran the logitr model on these groups using unique attributes.
| x | |
|---|---|
| value | 0.0003445 |
| penalty | -0.0011474 |
| accessibility_1 | 0.1270044 |
| accessibility_3 | 0.1178104 |
| accessibility_10 | -0.3246856 |
| incentive_grocery_store | 0.4872539 |
| incentive_internet | 0.1613869 |
| incentive_sport_tickets | 0.0789400 |
| outsideGood | 0.8599015 |
| value_low | 0.0004641 |
| penalty_low | 0.0008499 |
| accessibility_1_low | -0.0020351 |
| accessibility_3_low | -0.0912626 |
| accessibility_10_low | 0.1989877 |
| incentive_grocery_store_low | -0.0453873 |
| incentive_internet_low | 0.1695055 |
| incentive_sport_tickets_low | -0.1603067 |
| outsideGood_low | 0.3508514 |
| value_high | 0.0000381 |
| penalty_high | 0.0016762 |
| accessibility_1_high | -0.1538972 |
| accessibility_3_high | 0.0323651 |
| accessibility_10_high | 1.1737559 |
| incentive_grocery_store_high | 0.2786345 |
| incentive_internet_high | -0.4680978 |
| incentive_sport_tickets_high | 0.0701226 |
| outsideGood_high | 0.0912208 |
| x | |
|---|---|
| value | 0.0003284 |
| penalty | 0.0004240 |
| accessibility_1 | 0.3110987 |
| accessibility_3 | 0.3158577 |
| accessibility_10 | 0.3188004 |
| incentive_grocery_store | 0.3073506 |
| incentive_internet | 0.3248201 |
| incentive_sport_tickets | 0.3210829 |
| outsideGood | 0.3192067 |
| value_low | 0.0004380 |
| penalty_low | 0.0005801 |
| accessibility_1_low | 0.4253876 |
| accessibility_3_low | 0.4260864 |
| accessibility_10_low | 0.4409104 |
| incentive_grocery_store_low | 0.4235103 |
| incentive_internet_low | 0.4422044 |
| incentive_sport_tickets_low | 0.4435092 |
| outsideGood_low | 0.4493175 |
| value_high | 0.0005521 |
| penalty_high | 0.0006664 |
| accessibility_1_high | 0.5100550 |
| accessibility_3_high | 0.5017537 |
| accessibility_10_high | 0.5074341 |
| incentive_grocery_store_high | 0.4880623 |
| incentive_internet_high | 0.5299878 |
| incentive_sport_tickets_high | 0.5312624 |
| outsideGood_high | 0.5413572 |
We ran the logitr model with Asian, Black, Hispanic, and Native American respondents, who self-identified with each respective race. We obtained these groups by making dummmy columns for each race and marking each respondent with a 0 or 1 denoting if they fall in a particular ethnic group. Due to the fact that racial groups have unique biases, we could not merge racial groups and had to model Asians, Blacks, Hispanics, and Native Americans uniquely. We then ran the logitr model on these groups using unique attributes.
| x | |
|---|---|
| value | 0.0005767 |
| penalty | -0.0003769 |
| accessibility_1 | 0.1493537 |
| accessibility_3 | 0.0995948 |
| accessibility_10 | 0.0911452 |
| incentive_grocery_store | 0.2767176 |
| incentive_internet | 0.1446156 |
| incentive_sport_tickets | -0.0048925 |
| outsideGood | 1.1936386 |
| value_asian | -0.0004947 |
| penalty_asian | -0.0015811 |
| accessibility_1_asian | 0.1329034 |
| accessibility_3_asian | -1.1830281 |
| accessibility_10_asian | -1.0854955 |
| incentive_grocery_store_asian | 0.1798753 |
| incentive_internet_asian | -0.4854084 |
| incentive_sport_tickets_asian | -1.4625230 |
| outsideGood_asian | -3.3031581 |
| value_black | -0.0009031 |
| penalty_black | 0.0003545 |
| accessibility_1_black | -0.0174029 |
| accessibility_3_black | 0.1345240 |
| accessibility_10_black | 0.6291846 |
| incentive_grocery_store_black | 0.3805330 |
| incentive_internet_black | -0.6688335 |
| incentive_sport_tickets_black | 0.3029948 |
| outsideGood_black | -0.9511424 |
| value_hispanic | -0.0003316 |
| penalty_hispanic | -0.0003742 |
| accessibility_1_hispanic | -1.1800256 |
| accessibility_3_hispanic | 0.4838921 |
| accessibility_10_hispanic | -0.9817333 |
| incentive_grocery_store_hispanic | 1.7012276 |
| incentive_internet_hispanic | 2.1524001 |
| incentive_sport_tickets_hispanic | 2.2733010 |
| outsideGood_hispanic | 1.4130574 |
| value_native | 0.0010442 |
| penalty_native | -0.0015685 |
| accessibility_1_native | 0.2819474 |
| accessibility_3_native | 0.9975117 |
| accessibility_10_native | -0.0405203 |
| incentive_grocery_store_native | 2.0041884 |
| incentive_internet_native | 0.0333943 |
| incentive_sport_tickets_native | -0.8685169 |
| outsideGood_native | 0.0937451 |
| x | |
|---|---|
| value | 0.0002245 |
| penalty | 0.0002816 |
| accessibility_1 | 0.2193722 |
| accessibility_3 | 0.2187689 |
| accessibility_10 | 0.2200292 |
| incentive_grocery_store | 0.2149491 |
| incentive_internet | 0.2193150 |
| incentive_sport_tickets | 0.2229741 |
| outsideGood | 0.2317993 |
| value_asian | 0.0010520 |
| penalty_asian | 0.0013554 |
| accessibility_1_asian | 1.0890372 |
| accessibility_3_asian | 0.9494852 |
| accessibility_10_asian | 1.0216612 |
| incentive_grocery_store_asian | 1.1495492 |
| incentive_internet_asian | 1.2041302 |
| incentive_sport_tickets_asian | 1.1761442 |
| outsideGood_asian | 1.3543555 |
| value_black | 0.0006619 |
| penalty_black | 0.0009126 |
| accessibility_1_black | 0.6797840 |
| accessibility_3_black | 0.6524295 |
| accessibility_10_black | 0.6686678 |
| incentive_grocery_store_black | 0.6223760 |
| incentive_internet_black | 0.6939441 |
| incentive_sport_tickets_black | 0.6753842 |
| outsideGood_black | 0.6886401 |
| value_hispanic | 0.0011081 |
| penalty_hispanic | 0.0013126 |
| accessibility_1_hispanic | 1.0119626 |
| accessibility_3_hispanic | 0.8717907 |
| accessibility_10_hispanic | 1.0262839 |
| incentive_grocery_store_hispanic | 1.1957915 |
| incentive_internet_hispanic | 1.2885827 |
| incentive_sport_tickets_hispanic | 1.2897061 |
| outsideGood_hispanic | 1.2391928 |
| value_native | 0.0015067 |
| penalty_native | 0.0017405 |
| accessibility_1_native | 1.6091822 |
| accessibility_3_native | 1.3592319 |
| accessibility_10_native | 1.4966115 |
| incentive_grocery_store_native | 1.4367922 |
| incentive_internet_native | 1.2694133 |
| incentive_sport_tickets_native | 1.6821636 |
| outsideGood_native | 1.4531344 |
We ran the logitr model with poorly-educated and highly-educated respondents, who self-identified with each respective education level. We obtained these groups by making dummmy columns for each education level and marking each respondent with a 0 or 1 denoting if they have earned a particular educational degree. We decided to narrow down the number of groups to reflect our sample size by merging respondents who did not hold a Bachelor’s degree into the low-education group and merging respondents who earned a graduate degree into the high-income group. We then ran the logitr model on these groups using unique attributes.
| x | |
|---|---|
| value | 0.0003598 |
| penalty | -0.0006134 |
| accessibility_1 | 0.0661708 |
| accessibility_3 | -0.2540634 |
| accessibility_10 | 0.0879980 |
| incentive_grocery_store | 0.5380771 |
| incentive_internet | -0.2216536 |
| incentive_sport_tickets | 0.2010983 |
| outsideGood | 0.6532283 |
| value_low | 0.0002229 |
| penalty_low | 0.0001824 |
| accessibility_1_low | -0.0243030 |
| accessibility_3_low | 0.5894149 |
| accessibility_10_low | -0.1382827 |
| incentive_grocery_store_low | -0.0633475 |
| incentive_internet_low | 0.5188284 |
| incentive_sport_tickets_low | -0.2295321 |
| outsideGood_low | 0.6198316 |
| value_high | 0.0009449 |
| penalty_high | 0.0008621 |
| accessibility_1_high | 0.6248925 |
| accessibility_3_high | 0.5401381 |
| accessibility_10_high | 0.3625476 |
| incentive_grocery_store_high | 0.3688962 |
| incentive_internet_high | 0.9575404 |
| incentive_sport_tickets_high | -0.4836343 |
| outsideGood_high | 0.8185979 |
| x | |
|---|---|
| value | 0.0003061 |
| penalty | 0.0004300 |
| accessibility_1 | 0.2910949 |
| accessibility_3 | 0.3101552 |
| accessibility_10 | 0.2833100 |
| incentive_grocery_store | 0.2824072 |
| incentive_internet | 0.3118029 |
| incentive_sport_tickets | 0.3028663 |
| outsideGood | 0.3033357 |
| value_low | 0.0004043 |
| penalty_low | 0.0005356 |
| accessibility_1_low | 0.3956180 |
| accessibility_3_low | 0.3998634 |
| accessibility_10_low | 0.3944497 |
| incentive_grocery_store_low | 0.3812181 |
| incentive_internet_low | 0.4099136 |
| incentive_sport_tickets_low | 0.4066200 |
| outsideGood_low | 0.4137962 |
| value_high | 0.0008768 |
| penalty_high | 0.0009951 |
| accessibility_1_high | 0.7343250 |
| accessibility_3_high | 0.7455972 |
| accessibility_10_high | 0.7819365 |
| incentive_grocery_store_high | 0.7716949 |
| incentive_internet_high | 0.8025723 |
| incentive_sport_tickets_high | 0.8633954 |
| outsideGood_high | 0.8737230 |
We plotted the model coefficients and a 95% confidence interval for all continuous attributes, regressing attribute value onto utility from that attribute, with each coefficient being the slope. We also plotted each categorical attribute coefficient and a 95% confidence interval, with each coefficient representing the total utility from the respective attribute.
We plotted the baseline model attributes. We found that the general population had a favorable view towards incentives as utility is positively correlated with incentive value, but an unfavorable view for fines as utility is negatively correlated with penalty value. Moreover, respondents prefer grocery store gift cards the most. We also saw that respondents in general preferred vaccination centers within 3 miles of residences over door-door vaccinations.
We plotted the model attributes for resistant respondents. We found resistant respondents had an unfavorable view towards incentives as utility is negatively correlated with incentive value, and an unfavorable view for fines as utility is also negatively correlated with penalty value. However, resistant respondents seem to be slightly less sensitive to fines, but there is a high confidence interval for this attribute that spans 0, indicating that there is a high degree of uncertainty about the actual utility term for this attribute. Moreover, we found that resistant respondents. We also saw that respondents in general preferred door-door vaccinations and preferred cash as an incentive, however there is a high confidence interval for these attributes that spans 0, limiting our accuracy as to the actual coefficients.
We plotted the model attributes for conservative, liberal, and independent respondents. We found respondents in politic had an unfavorable view towards incentives as utility is negative correlated with incentive value, and an unfavorable view for fines as utility is also negatively correlated with penalty value. However, resistant respondents seem to be slightly less sensitive to fines, but there is a high confidence interval for this attribute that spans 0, indicating that profits could have 0. Moreover, respondents prefer grocery store gift cards the most. We also saw that respondents in general preferred vaccination centers within 3 miles of residences over door-door vaccinations.
We plotted the model attributes for low-income and high-income respondents. We found resistant respondents had an unfavorable view towards incentives as utility is negative correlated with incentive value, and an unfavorable view for fines as utility is also negatively correlated with penalty value. However, resistant respondents seem to be slightly less sensitive to fines, but there is a high confidence interval for this attribute that spans 0, indicating that profits could have 0. Moreover, respondents prefer grocery store gift cards the most. We also saw that respondents in general preferred vaccination centers within 3 miles of residences over door-door vaccinations.
Survey Link: https://covid-19-vaccine-policy-final.formr.org
Welcome to our survey!
Welcome and thank you for your interest in our survey! The purpose of this survey is to develop a quantitative model of consumer choice to inform the design decisions of a COVID vaccine policy. This survey is being conducted for a research initiative at The George Washington University. You must be a resident of the United States above the age of 18 and who has not received a dose of a COVID-19 vaccine to partake in this survey. You must also not have a health condition or disability that prevents you from being eligible to take the COVID-19 vaccine.
This survey involves questions that ask whether a certain condition would result in you receiving the COVID-19 vaccine. These conditions can be seen as positive, negative, or neutral. These conditions will not actually occur and you will not need to take a COVID-19 vaccine based on your answers in this survey. At the end of the survey we will also be collecting demographic information.
If you have any questions about this survey, then please contact marketingvaccinepolicy@gmail.com.
Your participation in this survey is voluntary. You may choose not to participate. If you decide to participate in this survey, you may withdraw at any time with no penalty. All responses will be used solely for academic purposes. Your responses will be confidential and we will not collect or share any identifying data such as your name or address.
The whole survey will take approximately 10 to 15 minutes to complete. Your participation is voluntary and you may stop the survey at any time. If you are eligible to and would like to participate, please answer the following questions:
I am age 18 or older.
I have not received any doses of a COVID-19 vaccine.
I am a U.S. resident.
I do not have a disability or health condition that prevents me from being able to take the COVID-19 vaccine.
I have read and understand the above information.
Great work! Now that you’ve shared a bit about yourself, we’d like you to consider policies designed to encourage COVID-19 vaccinations. Let’s learn about policy attributes.
Below is a description of the various policy attributes that will vary across every policy - each policy will be a combination of a universal vaccine mandate, enforced by a fine at tax season for not complying, different types of monetary incentives given out at tax season for being fully vaccinated against COVID-19, and the administration of vaccination clinics. Each attribute contains an associated image helping to explain the attribute.
Accessibility refers to the distance in which a vaccination clinic would be placed from homes. It varies with increasing increments from 0-10 miles from a constituent’s residence. A distance of 0 miles indicates door-door in-residence vaccination campaigns, a distance greater than 0 miles indicates that the policy involves a mobile or indoor public vaccination clinic.
Incentives refers to the type of commodity that would be given out at tax season as an incentive for being fully vaccinated against COVID-19 by the tax return deadline. It varies by cash (tax rebates) and cash equivalents (gift cards) redeemable at:
Incentive value refers to the monetary value of commodities that would be given out at tax season for being fully vaccinated against COVID-19 by the tax return deadline. It varies by increasing increments from $0 to $1000.
This value refers to the penalty due at tax season for failing to get a being fully vaccinated against COVID-19 by the tax return deadline under a vaccine mandate. It varies by increasing increments from $0 to $1000.
We would like to begin with one practice question below. Your response to this question will not be recorded.
If these were the only options available, would you choose to take the vaccine under the policy below?
Mandate: Vaccine mandated, $100 fine for non-compliance Accessibility: Vaccination clinic will be located within 3 miles from your house Incentive: $50 in cash"
Great work!
We will now begin with the questions. On the next few pages we will show you different combinations of policy options regarding COVID-19 vaccinations. There will be a total of 10 choice questions for you to answer. (Image shown below is an example of a choice question)
Nice job!
We’re almost done! We’d just like to ask just a few more questions about you which we will use solely to analyze our survey data.
Different identity (please state):
Different identity (please state):
We have reached the end. Please let us know if you have any other thoughts or feedback on this survey.
Your feedback will help us make future improvements :) Thank you so much for taking this survey.
(Open text response)